Monthly Dataset Research Articles

Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy

Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (r = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.

Unveiling the persistence of meteorological drought in Iraq: a comprehensive spatiotemporal analysis

Droughts, stemming from prolonged periods of scarce precipitation, lead to significant economic and societal consequences. Iraq has recurrently experienced the impact of severe droughts. This study assessed meteorological drought over Iraq, employing the Standardized Precipitation Index (SPI) on 12-month time scales from 2000 to 2022. The SPI values were calculated for 27 meteorological stations over Iraq based on the monthly datasets. The analysis of the SPI highlighted that the years 2000, 2008, 2009, 2010, 2012, 2018, and 2022 stood out as the driest in Iraq from 2000 to 2022. Notably, 2022 emerged as a particularly severe drought year, with the Ramadi weather station recording the maximum SPI value at -3.48. Different time scales in the analysis provided a more precise depiction of drought severity and frequency in Iraq. Namely, the findings resulting from this study offer a nuanced understanding of the magnitude, intensity, frequency, and duration of drought events in Iraq. This nuanced approach equips stakeholders with valuable insights for implementing more cost-effective mitigation and adaptation measures. This study contributes crucial information for understanding and addressing droughts challenges in Iraq. The comprehensive analysis aids in formulating targeted strategies to alleviate the impact of droughts on people and ecosystems.

(Re)Emerging disease and conflict risk in Africa, 1997-2019.

While the number of infectious zoonotic disease outbreaks has been rising, their impact on civil war and social conflict is poorly understood. This study addresses this fundamental limitation using a geolocated monthly dataset on 22 zoonotic diseases in Africa. Zoonotic disease is a key driver of new epidemics, making such pathogens a useful test case. Results suggest that over the January 1997 to December 2019 period, zoonotic disease was negatively associated with state initiation of civil conflict and positively associated with social conflict involving identity militias. Additional analyses find that the effect for identity militias is consistent with a causal interpretation. Rebel violence is not significantly associated with outbreaks. The results are robust to endogeneity concerns and additional sensitivity analyses.

Bank fundamental dynamics: the role of optimal bank management

Purpose This study aims to investigate the dynamic relationships between profitability, credit risk, liquidity risk and capital in Indonesian banking industry. Design/methodology/approach The authors use a panel vector autoregression model that incorporates macroeconomic variables: growth, interest rate, foreign exchange. The analysis is based on a monthly panel data set of 88 banks spanning from January 2012 to September 2021, which comprises 10,296 bank-month observations. Findings Our key findings highlight (i) permanent credit cost and liquidity cost pass through practices, (ii) complementary function of liquidity and capital, (iii) earning management motivated asset write off and (iv) credit risk-liquidity risk neutrality. In addition, the authors observe that the banks demonstrated resilience to macroeconomic shocks. Research limitations/implications Our study have shown some interesting dynamic patterns of fundamentals; nevertheless, unified theoretical underpinning of the process is still unavailable. This should be an important future reasearch avenue. Practical implications The study brings significant implications for regulatory and supervisory practices aimed at enhancing the financial stability of banks. Originality/value We conduct estimation of Indonesian banks system in dynamic perspective and perform impulses responses.

Relationship between monthly surface water derived from Sentinel-2 imagery and meteorological data (precipitation and evaporation) at Baghdad, Iraq

ABSTRACT This study was carried out using two main monthly datasets: satellite images and some meteorological parameters (like precipitation and evaporation) for 2 years (2018 and 2021). Using the fine spatial resolution images acquired from Sentinel-2, digital maps of surface water areas were produced by calculating the Modified Normalized Difference Water Index for all months in QGIS and then classifying them into two main categories (water and non-water bodies). The results show that the largest areas were in January 2021 (77.2 km2) and 40.7 km2 in November 2018, while the lowest (18.7 km2) were in June 2021 and July 2018. Monthly mean values of cumulative precipitation and evaporation were used to find their relationships with surface water areas. The results show that the year 2021 was a severe dry year, whereas the lowest precipitation (25 mm) in that year was associated with higher evaporation losses (3,021.8 mm) during this year. When combined with surface water areas and meteorological parameters, a non-linear relationship between water areas and evaporation was found for both years, and a linear relationship between precipitation and water areas for the wet year of 2018.

Substitutes or Complements? Evidence of the Relationship Between Air and Cruise Tourism

This article estimates the relationship between cruise and air tourism and their impact on economic growth in the Caribbean. To this end, we assemble a monthly data set of cruise and air tourist arrivals and a satellite-derived economic wealth proxy for 21 Caribbean islands over the period 2000‐2013. The estimates from a panel vector autoregression model not only demonstrate that cruise and air tourism are substitutes but it is the former for which this effect appears to be more permanent. Further, our analysis shows that while air tourism has a more immediate economic impact, cruise tourism has more long-term economic benefits. Our findings highlight the need for implementing policies to increase the value of cruise tourism to local economies as well as the effective management of revenue generation.

AFRIMA Model Forecast Performance: An Empirical Study using Naira-Yuan Exchange Rate

In financial time series and econometrics, some macroeconomic variables exhibit long memory features that may not be best described using short memory models like ARIMA. This paper, however, is structured to compare different fractional integration in AFRIMA forecast performance for the Naira-Yuan exchange rate. The empirical monthly data set used covered the period from January 1981 to December 2022. Fractional integration test are based on the ADF unit root test and the auxiliary autoregressive order three (AAR(3)) order of integration test. Model estimation is support by the Marquart algorithm for calculating least squares estimates and performance comparison is based on the Amaefula forecast criterion (AFC). The result specified that AFRIMA (1, d, 1) where I(d = 0.07891) is more appropriate and has the best forecast performance compared to others. The result also reveals that AFRIMA model yield better and more precise forecasts when fractional integration is closer to zero that is, I(d→0) than when I(d→½). Therefore, AFRIMA models can be useful in studying exchange rate dynamics for risk-averse and risk incline in times of investment and profitability in the long-run.

Historical Projection of the Rainfall Distribution in the North Region of Brazil

From a set of primary and secondary monthly data from 1991 to 2022, the annual and seasonal climatological pattern of the North region of Brazil, where most of the Amazon Biome is located, was analyzed. An incomplete Gamma distribution model was applied to the climatological precipitation data (mm); atmospheric pressure (mb); incidence of solar radiation (kJ/m²); air temperature and dew (ºC); relative humidity (%); wind intensity (m/s) with direction and maximum gust. Data normalization techniques and spatial similarity identified different patterns and trends of precipitation in the region, with significant seasonality, especially in terms of rainfall. The highest precipitation rates occurred in the N-NW axis, inside the northern Amazonian area, while the lowest values were observed in the southern equatorial zone, on the limits of the Pará State with the Cerrado Biome. Four zones and one climate range were established: southern, central, northern west and northern east zones and extreme north area, all with significant similarity in precipitation and humidity. A trend was observed in the number of days with precipitation between 25 and 50% in the southern and central zones, and an increase in precipitation of 50 to 75% for the northern zone. The wind distribution model showed a tendency towards the presence of isotropic climate phenomena, presenting the same properties or characteristics. The results obtained are relevant to support research aimed at understanding global warming and its consequences, in addition to contributing to forest management programs, and establishing more effective protocols for cultivation in agricultural areas in the Amazon.

Interpretable convolutional neural network for analyzing precipitation in the pre-rainy season of South China

Abstract The research and application of convolutional neural networks (CNNs) on statistical downscaling have been hampered by the fact that deep learning is highly dependent on sample size and is considered to be a black-box model. Therefore, a CNN model with transfer learning (CNN-TL) is proposed to study the pre-rainy season precipitation of South China. First, an augmented monthly dataset is created by sliding a fixed-length window over the daily circulation field and precipitation data for the entire year. Next, a base CNN network is pretrained on the augmented dataset, and then the network parameters are tuned on the actual monthly dataset from South China. Finally, guided backpropagation is conducted to obtain the distribution regions of the key features and explain the net. The coefficient of determination (R2) and root mean square error (RMSE) show that the CNN-TL model has higher explanatory power and better fitting performance than the feature extraction-based random forest (RF). Compared with the base CNN, the transfer learning approach can improve the explanatory power of the model by 10.29% and reduce the average RMSE by 6.82%. In addition, the interpretation results of the model show that the critical regions are primarily South China and its surrounding areas, including the Indochinese Peninsula, the Bay of Bengal, and the South China Sea. Furthermore, the ablation experiments and composite analysis illustrate that these regions are very important.

Forecasting food price inflation during global crises

AbstractIn this paper, we consider the forecasting of domestic food price inflation (DFPI) using global indicators, with emphasis on episodes of macroeconomic turbulence, namely, the Global Financial Crisis (GFC) and the COVID‐19 pandemic and its subsequent repercussions. Our monthly dataset covers about two decades for more than a hundred economies. We employ dynamic model averaging (DMA) to tackle both model uncertainty and parameter instability and produce pseudo out‐of‐sample forecasts. Thus, we are able to focus on the forecasting ability of the global predictors of DFPI before and during the global crises. We find evidence that the DMA specification tends to outperform statistical models frequently used in the literature such as random walks, autoregressive models, and time‐varying parameter models, especially during global crises. We also identify the most successful predictors during the crises using their posterior probabilities of inclusion. By comparing the distributions of such probabilities, we find that the international food price inflation is the most useful predictor of DFPI for numerous countries during both crises. Other indicators such as domestic CPI inflation as well as the international inflation of agricultural commodities, fertilizers, and other food categories improved their forecasting ability, particularly during the COVID‐19 period.

Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.

Impact of COVID-19 on migrant remittances in South Asia

ABSTRACT This paper investigates the impact of COVID-19 on remittance flows to South Asia. Using a monthly panel data set of five South Asian countries (namely Bangladesh, India, Nepal, Pakistan, and Sri Lanka) for the period January 2020 to August 2021. This study, applying the pooled ordinary least square estimation method, generally, finds a positive impact of COVID-19 on remittance flows to South Asia. Further investigation reveals the existence of a non-linear (inverted U-shaped) relationship between COVID-19 cases and remittances. This means that with the initial covid spread leading to temporary restrictions affecting economic activity in home countries, remittances start increasing to support families’ income and consumption affected by the regulations. However, the very high levels of infection rates leading to continued lockdowns in home countries decrease the incentives for investment with substantially reduced economic activities, thus decreasing aggregate remittances. We found similar results when replacing infection rates with deaths from COVID-19. However, increasing COVID-19 infection rates and deaths in migrants’ host countries reduce remittances to migrants’ home countries.

Assessing Monetary Policy Effects on Capital Markets: Evidence from the Euro Area

The focus of the paper is the influence the European Central Bank’s monetary policy has on the European capital market. The aim of this research is to investigate the relationship between monetary policy indicators and different segments of the capital market in the Euro area, particularly government, corporate bond and stock markets. We have used a standard structural vector autoregressive (SVAR) modelling methodology based on monthly dataset to evaluate the interrelations between observed six European variables. Impulse response functions and variance decomposition analysis have contributed to the model interpretation. Based on our estimations, we can conclude that each of the observed sectors of the European capital market is interrelated with ECB monetary indicators. The interest rate positive impulses’ impact on bond and stock markets’ volatility evolved mainly within a short period of time. Government, corporate and stock markets, in general, reacted positively to tight monetary policy, although each segment had its own behaviour throughout projection horizon. The outcome of variance decomposition analysis revealed that the fluctuations of the variables could more widely be explained by the shocks directed to themselves.

Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products

Abstract The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere. Significance Statement Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.

Assessing Effectiveness of Structural Monetary Policy in China

Abstract Structural monetary policy (SMP)—differentiated policy action targeting specific economic activity— has become more frequently applied by major central banks lately, although the practice of developmental central banking has been in existence for quite some time. The effectiveness of such structural monetary policy, however, has not been adequately evaluated. In this paper, we attempt to provide a comprehensive analysis of SMP introduced by the People's Bank of China and also to offer a broad framework for thinking about SMP. We first document the brief history and main components of China's SMP and estimate the narrow- and broad-measures of SMP accounting for 17.9 percent and 27.9 percent of base money, respectively, at the end of 2022. We then discuss two key factors determining success of SMP: bank credit flows to the targeted area and risk implications for banks. By applying the difference-in-difference and panel data analysis approaches using a monthly data set of city commercial bank loans, we evaluate the impacts of targeted required reserve ratio (RRR) cuts in 2019 and 2020 on small- and medium-sized enterprise lending. No statistically significant impact is found, whether in the short run or in the long run, and whether for medium-sized enterprises or for micro and small businesses). We conclude that, as no statistically significant impact of targeted RRRs are found, SMP's effectiveness, if exists at all, might be temporary; when facing impaired monetary policy transmission, it is important to conduct careful analysis of its effectiveness and sustainability.

Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea.

As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and LongShort-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.

Impact of the US unconventional monetary policy in times of crises on Vietnam

The study investigates the effects of the US unconventional monetary policy on the Vietnamese economy, focusing on the output and price index. Besides, the differences in the response of these indicators to the shock related to non-traditional tools implemented by the US in the Global Financial Crisis and during the pandemic crisis are also explored. By applying the Structural Vector Autoregressive model on the monthly dataset from January 2007 to April 2022, the results show that an expansionary monetary policy of the US by unconventional measures leads to a rise in the output and price index of Vietnam. In addition, risk-taking channels and portfolio rebalancing channels are important in the transmission of the US monetary policy to Vietnam, they cause booms in asset prices via the surge of capital flows. Moreover, another important finding is that the impact of US UMP on the Vietnamese economy during the Global Financial Crisis was generally two times higher than during the epidemic crisis.

Tourism and tax revenue: evidence from stay-over tourists in the Eastern Caribbean

ABSTRACT This paper investigated the impact of international stay-over arrivals on tax revenue for tourism-dependent Small Island Developing States (SIDS). To this end, we constructed a unique monthly panel data set of stay-over arrivals and tax revenue and its various components for the period 2002–2018 for Eastern Caribbean SIDS. A Bartik-type instrumental variables estimator exploiting differences in the exposure to and the extent of shocks in the origin markets was used to quantify the impact of stay-over arrivals on tax revenue and its different sources. Our results showed a large cumulative positive elasticity to tourism arrivals of 26.98% for total revenue, 26.86% for goods and services revenue, 23.62% for international trade and transactions revenue, 20.5% for income and profits revenue and 5.54% for property revenue. The findings demonstrate the region’s strong dependence on tourism for government income.

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Abstract. The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO2 (pCO2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO2 in the SCS with a reasonably high spatial resolution (0.05∘ × 0.05∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO2 constrained by EOF, atmospheric pCO2, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO2 field successfully simulated the spatial and temporal patterns of sea surface pCO2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO2 and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO2, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).